Private LLM deployments, autonomous workflow agents, and retrieval-augmented generation (RAG) knowledge systems.
Artificial Intelligence is essential for enterprise operations, but pasting proprietary corporate records into public LLMs creates grave compliance and security liabilities.
We implement private, enterprise-grade AI solutions: hosting open foundation models (Llama 3, Mistral) on dedicated hardware inside your isolated VPC, ensuring complete data privacy, zero public data retention, and instant semantic knowledge retrieval.
Staff uses public AI tools for work, exposing trade secrets, contract files and customer logs.
Engineers, support staff, or compliance teams spend hours reading documents to extract key specifications.
High-frequency manual admin work (like verifying invoices or reading logs) delays downstream cycles.
Teams looking to automate workflows using AI without sacrificing corporate data safety.
Businesses that must verify that AI systems strictly adhere to privacy standards.
When you must process sensitive client records, financials, or patents using AI.
When your internal documents, wikis and logs are too large for keyword search to yield accurate results.
Setting up open LLM inference servers on your isolated AWS/GCP account with API endpoints.
Parsing internal files, generating embeddings and building a secure QA chat interface.
Design and deployment of a private AI assistant or vector pipeline with set capabilities.
We analyze target business workflows, audit internal document structures, select appropriate model sizes, and model security boundaries.
We build automated document extraction pipelines, text chunking engines, and host vector index repositories (Pinecone/Qdrant/pgvector).
We deploy open-source models (Ollama/vLLM) on private GPU clusters and configure secure REST/GraphQL API endpoints for staff apps.
We build validation layers to intercept hallucinations, guard against prompt injection attacks, and verify zero data exit to external APIs.
We maintain your AI systems by managing model updates, cleaning vector indices and monitoring query costs to keep systems running smoothly.
The primary language for model setup, parsing pipelines and AI frameworks.
Inference runtime engines used to host open models with optimal GPU memory usage.
Vector databases for saving document embeddings and executing semantic queries.
No. Because the model is hosted within your private cloud environment, your data never leaves your networks and is not used to train external models.
We utilize Retrieval-Augmented Generation (RAG). Instead of relying on the model's memory, we feed it relevant excerpts from your documents and instruct it to only answer using the provided text.
Skip the generic sales calls. Speak directly with a KryoNex Solutions Architect to map your current architecture, identify engineering bottlenecks and design a scalable path forward.
Review your current tech stack and bounded contexts with a senior engineer.
Establish realistic milestones, engineering phases and capacity requirements.